The majority of the face morphing generation works are devised by performing the blending operation on the complete (or full) face images [1]}. To the best of our knowledge, there exist only one work that presented the part-based morphing attacks on FRS [2]}, where the blending operation is carried out using Poisson image editing [3]}. The generated composite morphs have shown vulnerabilities  in FRS based on deep-learning features such as VGGFace [4]}, Arcface [5]} and commercial-off-the-shelf (COTS) that includes Neurotech [6]} and Cognitec [7]}. Further, the human observer analysis is also discussed. However, the work presented in [2]} has several limitations, including (1) it is based on landmarks, and this would lead to pixel-based artifacts due to alignment issues, and correction of these would require manual intervention  [1]} (2) Only a few regions are used to generate the composite images (3) Limited only to the base regions like nose, mouth, eye and forehead. (4) Limited only to the single facial attribute composite generation (5) failure to achieve a high FRS vulnerability.
